{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"vscode-iterative-dvc","slug":"dvc-deprecated","name":"DVC (deprecated)","type":"extension","url":"https://marketplace.visualstudio.com/items?itemName=Iterative.dvc","page_url":"https://unfragile.ai/dvc-deprecated","categories":["automation"],"tags":["__ext_dvc","data version control","dataset","dvc","experiment tracking","hyperparameters","iterative","plots","snippet","yaml"],"pricing":{"model":"freemium","free":true,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"vscode-iterative-dvc__cap_0","uri":"capability://automation.workflow.experiment.tracking.with.git.integration","name":"experiment-tracking-with-git-integration","description":"Captures and organizes ML experiment runs (parameters, metrics, outputs) as Git commits, enabling version control of experiments alongside code. The extension reads DVC metadata files (.dvc, dvc.yaml) and Git commit history to reconstruct experiment lineage, displaying experiments in a hierarchical tree view within VS Code's Activity Bar. Each experiment is tied to a specific Git commit, allowing reproducibility by checking out historical commits.","intents":["I want to track multiple training runs with different hyperparameters and see which commit produced the best model","I need to reproduce an experiment from 3 weeks ago by checking out the exact code and data versions","I want to compare metrics across 10 different experiment runs without leaving VS Code"],"best_for":["ML engineers managing iterative training workflows in small-to-medium teams","researchers comparing experiment variants within a single project","solo developers prototyping models and needing lightweight experiment history"],"limitations":["Experiment tracking is Git-commit-based, so experiments must be committed to be tracked; uncommitted changes are not captured","No built-in distributed experiment tracking across multiple machines — requires manual synchronization via Git push/pull","Experiment comparison UI limited to VS Code viewport; large numbers of experiments (100+) may cause UI lag","Deprecation status means no new features or bug fixes will be released"],"requires":["Visual Studio Code (version unspecified in source, likely 1.50+)","DVC CLI installed and available in system PATH","Git repository initialized in the workspace","dvc.yaml or .dvc files present in the project"],"input_types":["YAML configuration (dvc.yaml, dvc.lock)","Git commit metadata","Metric files (JSON, CSV, or custom formats logged by training scripts)"],"output_types":["Hierarchical experiment tree view in VS Code Activity Bar","Experiment comparison tables (metrics, parameters)","Git commit references for reproducibility"],"categories":["automation-workflow","memory-knowledge"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"vscode-iterative-dvc__cap_1","uri":"capability://data.processing.analysis.data.versioning.with.remote.storage.sync","name":"data-versioning-with-remote-storage-sync","description":"Versions large files and datasets (outside Git's practical limits) by storing them in DVC's local cache and syncing to remote storage backends (S3, Azure Blob, GCS, NFS). The extension displays tracked data files in the Explorer View with version status indicators, allowing developers to pull/push specific datasets without cloning entire repositories. DVC uses content-addressable storage (file hashes) to deduplicate data across experiments and versions.","intents":["I want to version a 5GB dataset without bloating my Git repository","I need to switch between two different versions of training data for different experiments","I want to share large model checkpoints with teammates via S3 without manual file transfers"],"best_for":["ML teams working with datasets larger than 100MB","projects requiring multiple data versions for A/B testing or ablation studies","organizations with existing cloud storage infrastructure (AWS, Azure, GCP)"],"limitations":["Requires manual configuration of remote storage credentials; no built-in credential management UI in the extension","Data synchronization is not automatic — developers must explicitly run dvc pull/push commands","No bandwidth throttling or resumable downloads; large file transfers may block VS Code UI if run synchronously","Remote storage costs (S3, Azure, GCS) are the user's responsibility; DVC does not provide cost estimation or optimization"],"requires":["DVC CLI installed and configured with remote storage backend","Cloud storage account (S3, Azure Blob Storage, Google Cloud Storage, or NFS server) with credentials configured","dvc.yaml or .dvc files defining tracked data paths","Network connectivity to remote storage"],"input_types":["File paths (any format: CSV, Parquet, images, models, etc.)","dvc.yaml configuration specifying data dependencies","Remote storage credentials (AWS keys, Azure SAS tokens, GCS service accounts)"],"output_types":["Version status indicators in Explorer View (cached, remote, missing)","Synchronized local copies of data files","dvc.lock files recording data versions and hashes"],"categories":["data-processing-analysis","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"vscode-iterative-dvc__cap_10","uri":"capability://automation.workflow.experiment.checkout.and.reproducibility","name":"experiment-checkout-and-reproducibility","description":"Enables one-click checkout of historical experiments by switching to the corresponding Git commit and pulling the associated data versions. The extension reads the Git commit hash from the selected experiment and executes git checkout followed by dvc pull, restoring both code and data to the experiment's state. This allows developers to reproduce results or inspect experiment artifacts without manual command execution.","intents":["I want to reproduce an experiment from 2 months ago to verify the results","I need to inspect the model checkpoint and training logs from a specific experiment","I want to compare the code and data of two experiments side-by-side"],"best_for":["researchers requiring reproducible experiment workflows","teams auditing model training for compliance or validation","developers debugging issues in historical experiments"],"limitations":["Checkout operation modifies the working directory; unsaved changes are lost (extension should warn users)","Data pull may take significant time for large datasets; no progress indication or cancellation UI","Checkout only works for committed experiments; uncommitted changes cannot be restored","No automatic environment setup (Python dependencies, CUDA versions) — developers must manually ensure environment matches experiment"],"requires":["Git repository with experiment history","DVC project with tracked data versions","Clean working directory (or user acceptance of losing uncommitted changes)"],"input_types":["Selected experiment (Git commit hash)","Associated data versions from dvc.lock"],"output_types":["Checked-out Git commit","Pulled data files matching the experiment version","Restored project state"],"categories":["automation-workflow","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"vscode-iterative-dvc__cap_2","uri":"capability://data.processing.analysis.metrics.and.plots.visualization.dashboard","name":"metrics-and-plots-visualization-dashboard","description":"Renders interactive dashboards within VS Code displaying experiment metrics (loss, accuracy, F1 score) and custom plots (training curves, confusion matrices) side-by-side for comparison. The extension parses metrics from JSON/CSV files logged during training and overlays them on a configurable grid layout. Plots are updated in real-time as training runs progress, with support for filtering by experiment branch or commit.","intents":["I want to see training loss curves for 5 different hyperparameter configurations overlaid on the same graph","I need to compare final accuracy metrics across experiments without opening separate files","I want to monitor a live training run's metrics in real-time without switching to a terminal"],"best_for":["ML practitioners iterating on model architectures and hyperparameters","teams presenting experiment results to stakeholders within VS Code","researchers analyzing training dynamics and convergence patterns"],"limitations":["Plot rendering is limited to VS Code's WebView capabilities; complex 3D visualizations or interactive Plotly charts may have performance issues","Real-time metric updates require polling the metrics file; no event-driven updates, so latency may be 1-5 seconds behind actual training","Custom plot configurations must be defined in dvc.yaml; no GUI-based plot builder in the extension","Large datasets (100k+ data points per plot) may cause UI lag or memory issues in VS Code"],"requires":["Metrics files in JSON or CSV format logged during training","dvc.yaml configuration defining plot sources and axes","VS Code WebView support (standard in all modern VS Code versions)"],"input_types":["JSON or CSV files containing metrics (loss, accuracy, custom metrics)","dvc.yaml plot definitions specifying file paths, x/y axes, and grouping","Training logs or real-time metric streams"],"output_types":["Interactive line plots, scatter plots, and confusion matrices rendered in VS Code editor tabs","Comparison tables showing metric values across experiments","Real-time metric updates during training runs"],"categories":["data-processing-analysis","search-retrieval"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"vscode-iterative-dvc__cap_3","uri":"capability://automation.workflow.live.metrics.capture.during.training","name":"live-metrics-capture-during-training","description":"Monitors metric files (JSON, CSV) in real-time as training scripts write to them, updating the metrics dashboard in VS Code without requiring manual refresh. The extension watches the file system for changes to configured metric files and re-renders plots within 1-5 seconds of new data being written. This enables developers to observe training progress live without switching to terminal or external monitoring tools.","intents":["I want to watch my model's validation loss decrease in real-time as training progresses","I need to detect training divergence (loss exploding) immediately without polling a terminal","I want to compare live metrics across multiple parallel training runs on different machines"],"best_for":["ML engineers running long-duration training jobs (hours to days)","teams debugging training instability and needing immediate feedback","researchers monitoring hyperparameter sweep jobs across multiple GPUs"],"limitations":["File system watching has 1-5 second latency; not suitable for sub-second metric monitoring","Requires metric files to be written to local disk; remote training jobs must sync metrics back to the workspace","No built-in alerting or anomaly detection; developers must manually monitor for training failures","Watching many metric files simultaneously may consume significant CPU and memory in VS Code"],"requires":["Training scripts that write metrics to JSON or CSV files at regular intervals","dvc.yaml configuration specifying metric file paths","File system write access to metric files from training process"],"input_types":["Real-time metric file updates (JSON or CSV format)","Training process output written to configured metric paths"],"output_types":["Updated plots and metric values in VS Code dashboard","Real-time metric comparison tables"],"categories":["automation-workflow","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"vscode-iterative-dvc__cap_4","uri":"capability://automation.workflow.dvc.project.status.display.in.source.control.view","name":"dvc-project-status-display-in-source-control-view","description":"Adds a 'DVC' panel to VS Code's Source Control View showing the current state of tracked files and datasets (cached, remote, missing, modified). The extension reads DVC metadata and compares file hashes against the local cache and remote storage, displaying status indicators and file paths. This integrates DVC status alongside Git status, allowing developers to see both code and data versioning in one place.","intents":["I want to see which datasets are missing from my local cache before running an experiment","I need to understand why a data file is marked as modified in DVC","I want to quickly identify which large files need to be pushed to remote storage"],"best_for":["ML teams managing both code and data versions in a single workflow","developers new to DVC who need visual feedback on data versioning status","teams using DVC alongside Git and wanting unified version control visibility"],"limitations":["Status display is read-only; no direct actions (pull, push, remove) available from the Source Control View — users must use command palette","Status refresh requires manual trigger or file system watch; may not reflect recent remote changes until explicitly refreshed","Large projects with thousands of tracked files may have slow status computation"],"requires":["DVC project initialized with dvc.yaml or .dvc files","VS Code Source Control View visible (default in most setups)"],"input_types":["DVC metadata files (.dvc, dvc.yaml, dvc.lock)","Local file system state","Remote storage state (if configured)"],"output_types":["Status indicators in Source Control View (cached, remote, missing, modified)","File paths and version hashes"],"categories":["automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"vscode-iterative-dvc__cap_5","uri":"capability://tool.use.integration.dvc.command.palette.integration","name":"dvc-command-palette-integration","description":"Registers DVC commands in VS Code's Command Palette (accessible via Ctrl+Shift+P), allowing developers to execute DVC operations (dvc pull, dvc push, dvc repro, dvc dag) without opening a terminal. Commands are context-aware, operating on the current workspace or selected files. The extension translates user selections in the UI into corresponding DVC CLI invocations, capturing output and displaying results in the DVC output channel.","intents":["I want to pull the latest dataset version without switching to a terminal","I need to re-run a data pipeline (dvc repro) and see the output in VS Code","I want to visualize the dependency graph (dvc dag) of my data pipeline"],"best_for":["developers preferring GUI-based workflows over terminal commands","teams standardizing on VS Code as the primary development environment","users new to DVC who benefit from discoverability via Command Palette"],"limitations":["Command output is displayed in a text output channel; no interactive terminal for long-running commands","Complex DVC operations with many flags are difficult to express through the Command Palette UI; advanced users may prefer terminal","No command history or favorites; frequently-used commands must be re-typed each time","Deprecation status means no new commands will be added"],"requires":["DVC CLI installed and in system PATH","VS Code Command Palette accessible (Ctrl+Shift+P or Cmd+Shift+P)"],"input_types":["User selections in Command Palette","File paths (for context-aware operations)"],"output_types":["Command execution output in DVC output channel","Status messages and error logs"],"categories":["tool-use-integration","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"vscode-iterative-dvc__cap_6","uri":"capability://automation.workflow.dvc.tracked.files.explorer.view","name":"dvc-tracked-files-explorer-view","description":"Displays a hierarchical tree of DVC-tracked files and directories in VS Code's Explorer View, showing version status (cached, remote, missing) and file sizes. The extension reads .dvc and dvc.yaml files to populate the tree, allowing developers to navigate tracked data without using the terminal. Right-click context menus provide quick access to pull/push operations for individual files or directories.","intents":["I want to see all datasets tracked by DVC in my project at a glance","I need to pull a specific dataset without pulling all tracked data","I want to understand the size and version of each tracked file"],"best_for":["ML teams with many tracked datasets needing quick navigation","developers unfamiliar with dvc.yaml syntax who benefit from visual file browsing","projects with complex data dependencies requiring visual understanding"],"limitations":["Tree view may become unwieldy with hundreds of tracked files; no search or filtering within the tree","Context menu operations (pull/push) are limited to individual files; batch operations require command palette","File size display is static; does not update if remote storage changes without manual refresh"],"requires":["DVC project with .dvc or dvc.yaml files","VS Code Explorer View visible (default)"],"input_types":["DVC metadata files (.dvc, dvc.yaml)","Local file system state"],"output_types":["Hierarchical tree view of tracked files","Status indicators and file size information","Context menu for file operations"],"categories":["automation-workflow","search-retrieval"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"vscode-iterative-dvc__cap_7","uri":"capability://data.processing.analysis.experiment.comparison.across.metrics.and.parameters","name":"experiment-comparison-across-metrics-and-parameters","description":"Enables side-by-side comparison of experiments by displaying metrics and hyperparameters in a table format, with support for sorting and filtering by metric values or parameter ranges. The extension extracts parameters from dvc.yaml and metrics from dvc.lock or metric files, aligning them by experiment (Git commit). Developers can select multiple experiments and view their differences highlighted in the comparison table.","intents":["I want to find the experiment with the highest validation accuracy across 20 runs","I need to understand which hyperparameters had the biggest impact on model performance","I want to identify experiments that are statistical outliers (unusually good or bad results)"],"best_for":["ML researchers conducting hyperparameter sweeps and needing systematic comparison","teams presenting experiment results to stakeholders","practitioners analyzing the relationship between hyperparameters and performance"],"limitations":["Comparison table is limited to VS Code viewport; comparing 50+ experiments may require scrolling and is difficult to visualize","No statistical significance testing or confidence intervals; all metrics treated equally","Filtering and sorting are basic; no advanced query language for complex comparisons","Requires experiments to be committed to Git; uncommitted experiments cannot be compared"],"requires":["Multiple experiments tracked in Git history","dvc.yaml defining parameters and metrics","dvc.lock files recording parameter and metric values for each experiment"],"input_types":["dvc.yaml parameter definitions","dvc.lock metric and parameter values","Git commit history"],"output_types":["Comparison table with metrics and parameters","Sorted/filtered experiment lists","Highlighted differences between selected experiments"],"categories":["data-processing-analysis","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"vscode-iterative-dvc__cap_8","uri":"capability://planning.reasoning.dvc.pipeline.dependency.visualization","name":"dvc-pipeline-dependency-visualization","description":"Renders the DVC pipeline dependency graph (dvc dag) as a visual diagram within VS Code, showing data sources, processing stages, and outputs. The extension parses dvc.yaml to extract stage definitions and their dependencies, rendering them as a directed acyclic graph (DAG) with clickable nodes. Developers can click nodes to navigate to the corresponding stage definition in dvc.yaml.","intents":["I want to understand the data flow from raw data to final model output","I need to identify which stages depend on a specific dataset","I want to see the impact of changing a data processing step on downstream stages"],"best_for":["teams managing complex multi-stage data pipelines","researchers documenting data processing workflows","developers new to a project needing to understand data dependencies"],"limitations":["DAG visualization is static; does not update in real-time as dvc.yaml is edited","Large pipelines (50+ stages) may be difficult to visualize in a single diagram due to VS Code viewport constraints","No interactive features like zooming, panning, or layout customization","Requires dvc.yaml with explicit stage definitions; projects using only .dvc files are not supported"],"requires":["dvc.yaml file with stage definitions","VS Code WebView support for rendering the DAG diagram"],"input_types":["dvc.yaml pipeline definitions","Stage dependencies and outputs"],"output_types":["Visual DAG diagram rendered in VS Code editor tab","Clickable nodes linking to stage definitions"],"categories":["planning-reasoning","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"vscode-iterative-dvc__cap_9","uri":"capability://tool.use.integration.remote.storage.configuration.and.management","name":"remote-storage-configuration-and-management","description":"Provides UI for configuring DVC remote storage backends (S3, Azure Blob, GCS, NFS) through VS Code settings or a configuration wizard. The extension stores remote credentials securely using VS Code's secret storage API and validates connectivity to configured remotes. Developers can switch between remotes and view remote storage status without editing configuration files manually.","intents":["I want to configure S3 as my remote storage backend without editing .dvc/config manually","I need to switch between development and production S3 buckets for different experiments","I want to verify that my remote storage credentials are correct before pushing large datasets"],"best_for":["teams using cloud storage (AWS, Azure, GCP) for data versioning","developers unfamiliar with DVC configuration files","organizations requiring secure credential management"],"limitations":["Configuration UI is limited to basic remote setup; advanced options (custom endpoints, retry policies) require manual .dvc/config editing","Credential storage relies on VS Code's secret storage, which varies by platform (Keychain on macOS, Credential Manager on Windows, pass on Linux)","No built-in cost estimation or storage quota monitoring","Switching remotes requires manual selection; no automatic remote selection based on branch or experiment"],"requires":["Cloud storage account (AWS S3, Azure Blob Storage, Google Cloud Storage) or NFS server","Cloud storage credentials (AWS keys, Azure SAS tokens, GCS service accounts)","VS Code 1.50+ (for secret storage API support)"],"input_types":["Remote storage type selection (S3, Azure, GCS, NFS)","Storage credentials (access keys, tokens, service accounts)","Bucket/container names and paths"],"output_types":["DVC remote configuration stored in .dvc/config","Credentials stored securely in VS Code secret storage","Remote connectivity status and validation results"],"categories":["tool-use-integration","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":42,"verified":false,"data_access_risk":"high","permissions":["Visual Studio Code (version unspecified in source, likely 1.50+)","DVC CLI installed and available in system PATH","Git repository initialized in the workspace","dvc.yaml or .dvc files present in the project","DVC CLI installed and configured with remote storage backend","Cloud storage account (S3, Azure Blob Storage, Google Cloud Storage, or NFS server) with credentials configured","dvc.yaml or .dvc files defining tracked data paths","Network connectivity to remote storage","Git repository with experiment history","DVC project with tracked data versions"],"failure_modes":["Experiment tracking is Git-commit-based, so experiments must be committed to be tracked; uncommitted changes are not captured","No built-in distributed experiment tracking across multiple machines — requires manual synchronization via Git push/pull","Experiment comparison UI limited to VS Code viewport; large numbers of experiments (100+) may cause UI lag","Deprecation status means no new features or bug fixes will be released","Requires manual configuration of remote storage credentials; no built-in credential management UI in the extension","Data synchronization is not automatic — developers must explicitly run dvc pull/push commands","No bandwidth throttling or resumable downloads; large file transfers may block VS Code UI if run synchronously","Remote storage costs (S3, Azure, GCS) are the user's responsibility; DVC does not provide cost estimation or optimization","Checkout operation modifies the working directory; unsaved changes are lost (extension should warn users)","Data pull may take significant time for large datasets; no progress indication or cancellation UI","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.58,"quality":0.32,"ecosystem":0.35000000000000003,"match_graph":0.25,"freshness":0.75,"weights":{"adoption":0.25,"quality":0.25,"ecosystem":0.15,"match_graph":0.23,"freshness":0.12}},"observed_outcomes":{"matches":0,"success_rate":0,"avg_confidence":0,"top_intents":[],"last_matched_at":null},"maintenance":{"status":"active","updated_at":"2026-05-24T12:16:34.803Z","last_scraped_at":"2026-05-03T15:20:36.253Z","last_commit":null},"community":{"stars":null,"forks":null,"weekly_downloads":null,"model_downloads":null,"model_likes":null}},"distribution":{"claim_url":"https://unfragile.ai/submit?claim=dvc-deprecated","compare_url":"https://unfragile.ai/compare?artifact=dvc-deprecated"}},"signature":"r2UWgN86bgNNkDAJRHXPvB0LHehMZz0P7xAJsaSmO14M1B6oCReywcf672ro3lS3LLuaja8NQ+Hwg/PMYBULDg==","signedAt":"2026-06-22T14:10:36.149Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/dvc-deprecated","artifact":"https://unfragile.ai/dvc-deprecated","verify":"https://unfragile.ai/api/v1/verify?slug=dvc-deprecated","publicKey":"https://unfragile.ai/api/v1/trust-passport-public-key","spec":"https://unfragile.ai/trust","schema":"https://unfragile.ai/schema.json","docs":"https://unfragile.ai/docs"}}